1. Technical Field
The present invention provides a method of preventing harmful variability and oscillation in weight based, dynamic load balancing environments for distributing loads or service requests to a collection of computer servers or a server farm. More specifically, the present invention relates to methods to effectively determine the magnitude of weight changes in dynamic load balancing environments based on the workload magnitude and server farm capacity.
2. Description of Related Art
Copending U.S. patent application of Aman et al., Ser. No. 10/725,635, entitled: “Apparatus and Method for Determining Load Balancing Weights Using Application Instance Statistical Information”, filed Dec. 02, 2003, assigned to the International Business Machines Corporation, is incorporated herein by reference. This published patent application teaches a method of generating weights using application statistics.
Load balancers distribute load to a collection of machines to provide extended scalability and availability to applications. In large enterprise environments, multiple copies of applications on the machines or servers are used to service high numbers of requests and transactions. These environments use load balancers to distribute the incoming requests across the multiple copies of the enterprise application. Technologies such as the Server/Application State Protocol (SASP) enable third party products to continually advise the load balancers as to how incoming connections should be distributed. Distribution recommendations may be in the form of numerical weights where each weight represents the relative proportion of connections that should be given to a particular server or application. Many of the third party products that compute these dynamic weights do so based on current statistics related to the performance and resources used by the systems and applications in the server farm. The problem created by this type of computation is that various statistical measurements may cause a particular system or application to be heavily weighted at one point in time. When this happens under heavy load, an individual system can quickly be heavily loaded, causing the resulting statistical measurements to heavily favor another system. The result is an oscillating behavior where weights can heavily swing from one server or application to another. This oscillation causes degradation in the performance of the server farm.
Because of their limited insight into application and system performance, typical load balancers use static weights. Of the few instances where dynamic weights are used, there are no methods for reducing oscillations in the dynamic weights. While we have not found methods to reduce oscillation and variability in dynamic load balancing environments, there are methods of reducing oscillatory behavior in mathematical convergence techniques in the general field of mathematics. In these strategies, solutions are approached in steps. If the step is too small, the solution may take too long to reach. If the step is too large, oscillatory behavior can be seen around the solution. While we also look to avoid oscillatory behavior, our problem is different. In the dynamic load balancing environment, “the solution” would be the proper set of weights and this “solution” would be constantly changing. The anti-oscillatory methods in the mathematical convergence techniques in the general field of mathematics are to prevent the algorithm from oscillating around a static solution. These techniques usually involve some type of momentum characteristic to push the algorithm out of these oscillations and closer to the goal. In the dynamic load balancing case, the goal would change by the time the momentum was applied possibly causing an action in an undesired direction. The other problem with the mathematical convergence oscillatory prevention mechanisms is that they may require significant changes to existing weight generation products, such as workload managers.
There is, therefore a need to achieve a balance between very conservative weight changes that will react very slowly to server farm or workload changes and large weight changes that will react too abruptly to server farm or workload changes.